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A. Lekova, A. Krastev and I. Chavdarov

Abstract

In the context of learning new skills by imitation for children with special educational needs, we propose Wireless Kinect-NAO Framework (WKNF) for robot teleoperation in real time based on Takagi-Sugeno (T-S) Fuzzy Inference System. The new solutions here are related to complex whole-body motion retargeting, standing body stabilization, view invariance and smoothness of robot motions. The raw depth Kinect data are fuzzified and processed by median filter. The joint angles estimation for motion mapping of Human to NAO movements is based on fuzzy logic and featured angles rather than direct angles are calculated by Inverse Kinematics due to differences in the human and robot kinematics. During the joint angles calculation nonlinearities are observed as a result of ambiguity of Kinect 3D joint coordinates in different offsets. NAO kinematic limitations and nonlinearities in workspace are decomposed and linearly approximated by T-S fuzzy rules of zero and first order that have local support in 2D projections. To prevent the robot to fall down, the center of mass is considered in order NAO to stay within a support and safe polygon. The feasibility of the proposed framework has been proven by real experiments.

Open access

A. Lekova, D. Ryan and R. Davidrajuh

Abstract

The paper presents enhancements and innovative solutions of the proposed in [3] algorithms for fingers tracking and hand gesture recognition based on new defined features describing hand gestures and exploiting new-tracked tip and thumb joints from Kinect v2 sensor. Dynamic Time Warping (DTW) algorithm is used for gestures recognition. We increased its accuracy, scale and rotational invariance by defining new 3D featuring angles describing gestures and used for training a gesture database. 3D positions for fingertips are extracted from depth sensor data and used for calculation of featuring angles between vectors. The provided by Kinect v2 3D positions for thumb, tip and hand joints also participates during the phases of recognition. A comparison with the latest published approach for finger tracking has been performed. The feasibility of the algorithms have been proven by real experiments.